| Autor: |
Kamil Malinka, Anton Firc, Milan Šalko, Daniel Prudký, Karolína Radačovská, Petr Hanáček |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
EURASIP Journal on Image and Video Processing, Vol 2024, Iss 1, Pp 1-25 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1687-5281 |
| DOI: |
10.1186/s13640-024-00641-4 |
| Popis: |
Abstract In this paper, we undertake a novel two-pronged investigation into the human recognition of deepfake speech, addressing critical gaps in existing research. First, we pioneer an evaluation of the impact of prior information on deepfake recognition, setting our work apart by simulating real-world attack scenarios where individuals are not informed in advance of deepfake exposure. This approach simulates the unpredictability of real-world deepfake attacks, providing unprecedented insights into human vulnerability under realistic conditions. Second, we introduce a novel metric to evaluate the quality of deepfake audio. This metric facilitates a deeper exploration into how the quality of deepfake speech influences human detection accuracy. By examining both the effect of prior knowledge about deepfakes and the role of deepfake speech quality, our research reveals the importance of these factors, contributes to understanding human vulnerability to deepfakes, and suggests measures to enhance human detection skills. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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